{"paper_id":"39b5b04e-66c5-481a-a447-86d1fb0b89db","body_text":"Embedding of taggants in the vat photo-polymerisation additive manufacturing process for anti-counterfeiting measures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Embedding of taggants in the vat photo-polymerisation additive manufacturing process for anti-counterfeiting measures Bochuan Liu, Paul F. Wilson, Mark A. Williams, Gregory J. Gibbons This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6627651/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Additive Manufacturing (AM) industry has grown significantly and attracted global attention. The unique nature of AM, that it can manufacture components from CAD data, made it vulnerable to fraudulent and counterfeit activities. With scanning and reverse engineering technologies advancing, existing authentication methods (such as embedded Quick Response code) become easy to detect and reproduce, and therefore unreliable. This article investigates a novel authenticating method for vat photo-polymerisation process with a double-lock system introduced. Both physical hash (the taggants) and digital hash (generated by X-Ray computed tomography scan) were embedded into each component. A number of digital hashing methods were proposed, including using a combination of the taggant spatial coordinates, colour code and quadrat count, which are impossible to reproduce. Using a 100µm layer thickness, the χ 2 value of the distribution can also be used as the digital hash. This study also indicated the embedded physical taggants had no effect on the thermal properties of the parts, and had minimal impact on the mechanical strength, with reductions of 3.1 ± 0.5%, 3.9 ± 0.5% and 1.1 ± 0.5% in UTS at room temperature for the 100, 50 and 25 µm layer thicknesses observed. The UTS for all samples decreased with increasing temperature The existence of the taggant did not affect the UTS within statistically significant levels, except for those tested at 23 o C and 180°C with 3.1 ± 0.5% and 17 ± 2% observed variation in UTS between samples with and without taggants. Additive manufacturing Vat photo-polymerisation Anti-counterfeiting Taggant X-Ray computed tomography Double-lock hash Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Additive manufacturing (AM), also known as 3D printing, has gained significant interest from industry and academia [ 1 ]. Since the 1980s, a spectrum of AM technologies has been developed [ 2 ], and among these types of AM processes, vat photo-polymerisation (VPP) is one of the most popular techniques that has obtained attention from a vast range of the fields including medicine and dentistry, biomedical engineering, metamaterial, injection moulding, jewellery, and consumer products, etc. [ 3 – 6 ]. The principle of VPP is to cure the photosensitive liquid resin layer by layer to fabricate a solid part [ 7 ]. The rapid adoption of AM technology has an undesirable side effect, where counterfeit parts have become easier to obtain provided they have the suitable computer-aided design (CAD) models and 3D printers [ 8 ]. The CAD models can be downloaded from the internet or obtained from reverse engineering, and nowadays many 3D printers are low-cost and easily accessible. This AM supply chain integrity weakness could result in premature failure of a counterfeit component manufactured by AM, resulting in image, brand and financial loss and damage to a manufacturing company [ 9 ]. As a consequence, methods to embed various security features in AM parts have been developed. Some of these are embedded in the 3D CAD files in the design step [ 10 ], and some are embedded in the printed components, such as fluorescent quantum dots [ 11 ], directly printed quick response (QR) code [ 12 ], multiple-material QR code [ 8 , 13 ], surface fingerprint [ 14 ], crystallographic orientation [ 15 ] and polymer crystallinity [ 16 ], etc.. One of the popular protections in the forensics field is the taggant, which allows an object to be identified, tracked, and traced [ 17 ]. Taggants are usually introduced into components at a dopant level of 5% or less, making them generally affordable [ 18 ]. However, the main disadvantage of taggants is they are easy to detect and replicate if too simple and obvious [ 19 ]. The wide availability and low cost of taggant materials makes it easier to be duplicated. This study aims to develop a “double lock” system to embed taggants into the VPP printed components. The first lock exists physically, and the second one exists digitally, leading to a security feature nearly impossible to reproduce, but easy to detect. This work could help to decrease the difficulty to identify counterfeit goods and prevent damage to the manufacturing companies. The distribution of taggants within the parts, and their effect on the printed part’s mechanical and thermal properties are investigated. 2. Materials 2.1 Taggant Since the primary aim of this research is to create a practicable methodology to embed a “hash” in the VPP process, benefiting the AM supply chain, a commercially available forensic taggant is preferred for this purpose. Existing forensic taggants include physical, spectroscopic, chemical and DNA taggants with different analysis methods, uses, advantages and disadvantages [ 17 ]. Microtaggant® (Microtrace LLC, USA), a physical taggant which contains multiple levels of security within each particle was chosen for this study. As a physical taggant, it is made unique by a specific size, appearance, or structural arrangement [ 20 ]. Such encoding mechanisms are also described as “graphical” as the detection and analysis is usually achieved by visual methods [ 21 ]. Microtaggant® comprises a colour layer sequence, shown in Fig. 1 , where the colour layer can be converted to a unique numeric code (Table 1 ). The code can then be registered against a particular owner on an electronic database [ 22 ]. Table 1 Microtaggant®’s colour coding values suggested by Microtrace, but can be user-defined 0 Black 5 Green 1 Brown 6 Blue 2 Red 7 Violet 3 Orange 8 Grey 4 Yellow 9 White Microtaggant® is also paired with infrared radiation (IR), ultraviolet (UV) and afterglow features, as shown in Fig. 2 . This enables investigators to perform on-the-spot, non-destructive field testing with handheld detectors. Microtaggant® is stable at up to 250°C, and up to 350°C for short exposure, which can be used in explosives detection [ 17 ]. No licensing agreement is required to use Microtaggant®. Microtrace provides Microtaggant® in a range of particle sizes from 20 to 1,200 µm. Considering the VPP’s layer thickness to be used in this study (25–100 µm), particle sizes between 38 to 75 microns were acquired. This size range gave roughly 2 million particles per gram. 2.2 Resin A triacrylate-based amorphous resin - HighTemp DL400 (PhotoCentric Ltd, UK) was used in this study. It appears as a transparent amber liquid and has heat deflection temperature (HDT) of 230°C when fully cured. This transparency allows the Microtaggant® to be detected by optical microscopy when embedded inside the resin parts. The high HDT allows the cured parts with taggants to be tested at an elevated temperature close to the stable limit of the Microtaggant®. 3. Experiment Methods 3.1 Overview Experimental Plan The experimental plan consists of 3 main steps: 1) print samples with taggants embedded on a VPP 3D printer with different process parameters; 2) detect the taggants with various technologies and quantify the taggants inside the samples. This step will provide the information for a “double lock” security design – physically embedded hash (the taggants) and encrypted and hashed digital files (numeric codes); 3) evaluate the mechanical and thermal properties of the printed samples with taggants embedded. 3.2 3D printing 3.2.1 Resin Preparation The HighTemp DL400 was warmed in a Gravity Convection Oven (Fisher Scientific Ltd, UK) at 60°C for 5 hours prior to printing to dissolve any crystallised resin that can occur at or below room temperature. After the resin was fully liquified, Microtaggant® was added to the resin and stirred at 60°C and 500 rpm for 20 minutes on a AM4 Heating Magnetic Stirrer (VELP Scientifica Srl, Italy). The taggants loading rate was 0.01 g per 100 ml resin, leading to roughly 20,000 particles in each 100 ml resin. 3.2.2 Printing Samples Samples were printed using a Liquid Crystal Nano (Photocentric Ltd, UK), a bottom-up mask projection stereolithography (MPSL) 3D printer using a liquid crystal display mask (LCD) with 4K resolution. Three layer thicknesses were used to compare the quantity of embedded taggants and their effect on the parts’ quality. Different process parameters were assigned to each layer thickness to fully cure the resin, but also to avoid over cure and prolonged printing time (Table 2 ). Energy density per unit power was calculated by Eq. 1. \\(\\:\\raisebox{1ex}{$unit\\:power\\:1W*exposure\\:time$}\\!\\left/\\:\\!\\raisebox{-1ex}{$layer\\:thickness$}\\right.\\) Eq. 1. Table 2 Printing process parameters for each layer thickness Layer thickness (µm) Exposure time for each layer (ms) Energy density per unit power (J/mm) 100 2,500 25 50 1,500 30 25 1,000 40 After the printing process, all the samples were washed for 10 minutes using Tripropylene glycol monomethyl ether (TPM), then rinsed with tap water for 2 minutes. The samples were then removed from the build substrate, wiped and air-dried before loaded into a 400 nm ultraviolet-visible (UV-Vis) oven (Cure L2 - Photocentric Ltd, UK) with 60°C heating for 2 hours. Tensile test specimens designed following ASTM D638 Type V [ 23 ] were built using the parameters in Table 2 (16 for each layer thickness). These specimens were built in the upright position, with the overall length in the Z build direction; and were built directly on the building substrate with no support structures. 8 tensile specimens were built for each layer thickness parameter set with embedded taggants, and 8 without. Additional tensile specimens were built for static tensile testing at elevated temperatures (12 for each test temperature, 6 with embedded taggants and 6 without). These were also built in the upright position and directly on the building substrate at 100 microns layer thickness only. In the same printing job of tensile specimens at 100 microns layer thickness, 36 ∅5 x 1.5 mm discs were added for the thermal analysis, 18 of the discs were embedded with taggants and 18 without. 3.3 Taggants Detection 3.3.1 Visual Detection A 365 nm portable UV light and a 980 nm laser pen (Microtrace LLC, USA) were used to detect if the taggants had been successfully embedded into the parts. A VHX7000 digital optical microscope (Keyence (UK) Ltd, UK) was used to obtain the numeric value from the colour layer sequence for each individual taggant. 3.3.2 X-Ray Computed Tomography X-ray computed tomography (XCT) was used to extract and characterise the size, number and position of taggants within the samples, a technique perfectly suited to evaluating and quantifying materials across engineering sectors [ 24 – 27 ]. A total of 9 different tensile test samples were scanned using XCT, three at each of the layer thickness conditions that can be observed in Table 2 , 100µm, 50µm, and 25µm. The central-most section of each was scanned on a Tescan Unitom XL system (Tescan-Orsay, Brno), at the Centre for Imaging, Metrology, and Additive Technologies (CiMAT). All samples were scanned at the same settings using the proprietary scanning software, Acquila (Tescan-Orsay, Brno), using a polychromatic source with a tungsten target. A beam voltage of 80kV was used at a power of 15W. A total of 2279 projections were acquired with a Source-Detector Distance (SDD) of 1250mm, an exposure time of 0.51s, 2 frame averages and at a voxel size of 3µm. The datasets for all nine samples were reconstructed in Panthera (Tescan-Orsay, Brno) using a standard FDK algorithm [ 28 ], producing a set of tiff image stacks for each sample representing a 5mm section of the centre of each sample. These tiff stacks were then analysed in Avizo 2021.2 (Thermo-Fisher Scientific, Waltham) to extract a number of properties for each sample, namely: 1) the volume fraction of taggants; 2) the number of taggants; 3) the volume and position of individual taggants. For all samples, individual taggants were segmented in Avizo using an Interactive Threshold module, with variable settings used to select them as can be observed in Table 3 . Then, segmented noise was removed using an Opening module with 2px cube structuring element with a Neighbourhood of 18px. The surrounding resin was then segmented using the Wand Tool in the segmentation editor of the main resin body, whose settings can be seen in Table 3 also, with a sequence of dilation and erosion of the area to remove noise and fill holes. This produced both label fields for formal analysis. Volume fractions were calculated as a percentile of each fraction to the total volume of both fractions. Volume and position characteristics were extracted using the Label Analysis module, extracting Volume3d, BaryCenterX, BaryCenterY, and BaryCenterZ. Table 3 Thresholding Parameters for Fractions in all Samples in Avizo. Sample Taggant Threshold Resin Threshold 100µm #1 21399–65535 9773–21398 100µm #2 15158–65535 5479–15159 100µm #3 18724–65535 7473–18723 50µm #1 31653–65535 17533–31652 50µm #2 38340–65535 18971–38339 50µm #3 22291–65535 9198–22290 25µm #1 34328–65535 20408–34327 25µm #2 31207–65535 18971–31206 25µm #3 23182–65535 11497–23181 3.4 Property Evaluation The room temperature tensile test samples were tested to destruction by static tension testing at 1 mm/min using an Instron 3367 test system (Instron UK Ltd, UK) with 30kN static loading cell and Instron 2630 − 102 clip-on extensometer. The elevated temperature tensile testing was performed at 1 mm/min using an Instron 5985 test system (Instron UK Ltd, UK) with 10kN static loading cell, environmental chamber and Instron 2663 − 821 advanced video extensometer, testing at temperatures 23, 60, 100, 140, 180 and 220°C to destruction. When the environmental chamber reached the desired testing temperature, the samples were allowed an additional 5 minutes soaking time before the test start. 6 disc samples (3 with taggants and 3 without) were stored inside the environmental chamber at the same time while tensile testing was carried out, to obtain the same thermal history as the tensile specimens. Differential Scanning Calorimetry (DSC) was performed on the disc samples using a DSC 1 (Mettler Toledo UK, UK) thermal analysis system. The samples were measured in aluminium pans with lids and heated under a nitrogen atmosphere from 25°C to 250°C at 20°C/min. Upon reaching 250°C, the samples were heated isothermally for 5 minutes, then cooled down to 25°C at 20°C/min. The same method was used on printed resin samples with and without taggants. 4. Results and Discussion 4.1 Taggants Detection 4.1.1 Taggant Existence Checked by Visual Detection In the tensile test specimens built using 25, 50 and 100 µm layer thickness, the taggants were detected in all the samples using 980nm light exposure. They were also detected using 365nm UV light exposure. These observations demonstrate that the Microtaggant® can be embedded in the resin parts using the VPP process, even when the layer thickness is smaller than the taggants’ particle size. When printing at 25 µm layer thickness, the taggant was transported into the gap between the aluminium build plate and the polymer vat sealing film when the gap was much greater than 25 µm during the resin layer filling stage of the VPP process cycle, and was then pushed into the soft vat film or previously printed layers when the curing gap was closed down to the 25 µm layer thickness. The embedded taggant, shown in Fig. 3 , gave the numeric values to form the first part of the digital hash. 4.1.2 Volume Fraction of Taggants The volume fraction of taggants for each sample can be found in Table 4 . Overall, the volume fraction of taggants in each sample is very low, but appears to increase with coarser layer thickness. Table 4 Volume Fraction of Taggants in each Sample Sample Layer Thickness Volume Fraction of Taggants (%) Volume Fraction of Resin (%) 25 µm 0.0004 ± 0.0002 99.9996 ± 0.0002 50 µm 0.0008 ± 0.0004 99.9992 ± 0.0004 100 µm 0.0048 ± 0.0008 99.9952 ± 0.0008 4.1.3 Dimensions, Quantity, and Position of Taggants The number of taggants and their volumetric data can be found in Table 5 . Overall, the number of taggants appears to scale with the layer thickness, with coarser thicknesses resulting in the retainment of more taggants. The size of taggants in each sample appears to be quite variable however, with a wide variety of taggant sizes being present in each sample with a large standard deviation for each. This appears to imply overall that coarser layer thicknesses result in greater retention of taggants, but the size of those taggants is also variable. Table 5 Properties of Taggants in Samples Sample No. of Taggants Average Volume of Taggants (mm 3 ) x10 − 5 100µm #1 18 9 ± 7 100µm #2 20 15 ± 10 100µm #3 27 9 ± 6 50µm #1 6 4 ± 3 50µm #2 5 5 ± 3 50µm #3 12 6 ± 4 25µm #1 1 10.1 25µm #2 7 2 ± 5 25µm #3 4 10 ± 7 4.1.4 Spatial Point Analysis The position of taggants on each sample can also be observed in Fig. 4 . A quadrat statistical analysis was used to examine the evenness of distribution of the taggants. By applying a 3 x 3 grid, the count of observations that fall within a given cell is presented in Fig. 5 . Taggant location appears to be biased towards the corners of each sample, with relatively few taggants being apparent within the centre of the samples. This also appears to be related to the layer thickness, with more central taggants being apparent within the coarser layer thickness samples. Testing the observed quadrant distributions for randomness, modelling each distribution using the Poisson distribution and performing a c 2 test for randomness between the expected and observed distributions was performed. The results of this test for each distribution are given in Table 6 . The c 2 test is made for the 5% probability level. The test was performed using a 5 x 5 square quadrant. The analysis was performed in R (The R Project), using the “quadrat.test” function within the Spatstat spatial point pattern analysis tool. From this analysis, none of the 25 µm sample distributions are random (p > 0.05, null hypothesis is rejected) and only one sample for the 50 µm distributions (Sample 3) is random (p < 0.05, null hypothesis is accepted). For the 100 µm samples, all three are statistically random (p < 0.05). Table 6 Results of c 2 test for randomness for the 25µm, 50µm and 100µm distributions Sample Test Sample 1 Sample 2 Sample 3 25 µm c 2 25.143 21.000 24.000 p 0.796 0.723 0.923 50 µm c 2 27.333 20.000 42.167 p 0.578 0.606 0.025 100 µm c 2 47.556 47.400 48.000 p 0.006 0.006 0.005 4.1.5 Double Lock System From the XCT data, the position of the embedded taggants can be obtained and presented in XYZ coordinates. These coordinates values form the second part of the digital hash. For example, a taggant with colour code “09290” was detected in the part at X = 3.046 mm, Y = 2.576 mm and Z = 1.367 mm would be assigned a unique digital code: 09290304625761367. Another digital hash can be obtained from quadrat statistics of the taggant distribution in any given sample. For sample 100µm #3, the count of taggants is presented in Fig. 6 . Combining the number from top left cell to bottom right cell could generate a digital code: 233306631. As can be seen from Table 6 , it would only be possible to use the c 2 value as a digital lock for 100 µm samples as it is too variable between samples prepared using the same methodology and taggant dosing level for 25 and 50 µm layer thickness. This is as a result of the low level of taggants found in the spatial distributions in these samples. Figure 7 illustrates the taggants embedding and detection process for a printed component. At the manufacturer side, after the printing stage, XCT scan is preformed to quantify and locate the taggants and their spatial distribution, creating the 2nd part of the digital hash. The taggants’ colour code is then obtained using optical microscopy to complete the digital hash for each taggant. The component and the digital hash are then sent to the customer separately, while the digital hash can be stored on a blockchain. When the customer receives the product, decoding will be performed first to locate the taggants in the component, then using the visual detection methods to check if the taggants are in the correct place, and if the colour code matches the digital hash. In some geometry features that optical microscopy cannot obtain an image, or in non-transparent resin parts, it would be difficult to obtain the numeric value from the taggants’ colour layer. In this case, the position coordinates and spatial point analysis acquired from XCT will form the digital hash, and to decode and trace the taggant, XCT scanning would be required at the customer end. This approach also applies to the digital hash generated by quadrat statistics as both ends need to perform XCT scanning to match the code. To ensure the method is scan-agnostic, a master taggant will be identified, and other taggants will have coordinates relative to the master taggant. This is to avoid end users using a different scanning coordinate system from the manufacturer side and generating different results. 4.2 Thermal analysis Figure 8 illustrated the heat flow comparison for the 100 µm layer thickness printed samples with and without taggant embedded, and no significant difference was observed. Although the taggant is a different material and could have different material properties, it did not change the thermal properties of the resin samples due to very low concentration and non-reactivity between the taggant and the resin. Since the taggant may have different thermal conductivity, it could act differently when preheated. Samples which were pre-soaked in the elevated temperatures were tested in DSC and the heat flow results from 60 and 220°C pre-soak samples are presented in Fig. 9 . These samples were selected as they presented two ends of the elevated temperature range. As for the non-preheated samples, embedded taggants did not cause visible differences in the thermal properties of printed resin parts. 4.3 Static Tensile Testing Figure 10 shows the Ultimate Tensile Strength (UTS) values of tensile test specimens built under each layer thickness, with and without taggants. Without embedded taggants, samples built with layer thickness 50 µm compared to the 100 µm showed an increase in UTS from 83.4 ± 0.4 MPa to 85.1 ± 0.5 MPa, could be due to the increased energy density [ 29 ]. However, samples built with layer thickness 25 µm compared to the 50 µm showed a slight drop in UTS from 85.1 ± 0.4 MPa to 84.8 ± 0.5 MPa, even when the energy density is higher, although this change is within the bounds of the statistical error, so is insignificant. Lower layer thickness would cause the curing light source to penetrate the cured layer, and the previously cured layers would be exposed multiple times. This could induce internal stress in the samples during the printing process [ 30 ], and affect the mechanical strength. The VPP process utilises the liquid photopolymer to undergo chemical reaction and rapid curing under certain light irradiation. With taggants existing in the liquid resin, photoinduced polymerisation and chemical reaction could be interrupted or impaired [ 31 ]. The embedded taggants inside the parts might also prevent further crosslinking during post-curing. This was evidenced by the tensile test results, with higher taggant densities resulting in lower mechanical performance. The 100 µm layer sample had the most taggants, and displayed the lowest UTS; the 25 µm layer sample had the least taggants, and displayed the largest UTS. All the samples with taggants inside presented lower UTS comparing with their same layer thickness counterparts, with reductions of 3.1 ± 0.5 %, .9 ± 0.5 % ad 1.1 ± 0.5 % fr the 100, 50 and 25 µm layer thicknesses. The lower % of taggants in the 25 µm layer thickness samples may have overcome the potential internal stress caused by multiple exposures, and led to a higher UTS compared to the 50 µm layer thickness samples. The tensile tests at elevated temperature were carried out on 100 µm layer thickness samples only, and the results are presented in Fig. 11 . Similar to other polymer materials, the resin samples’ UTS decreased with increasing testing temperature [ 32 ]. The existence of the taggant did not affect the UTS within statistically significant levels, except for a 3.1 ± 0.5 % (3 o C) and a 17 ± 2 % (80°C) relative difference in UTS between the samples with and without taggants, although this variation could be due to the small sample size (6 samples per test set). 5. Conclusions This study has proven that the Microtaggant® can be successfully embedded in the resin parts via the vat photo-polymerisation process at 25, 50 and 100 µm layer thickness. The taggants can be successfully detected by optical methods and XCT. Both physical hash and digital hash generated in this study produced a “double lock” system which is impossible to reproduce. XCT scanning has shown that the quantity of taggants within each sample was largely dependent on the layer thickness, which also has an influence on the distribution of them within each sample. The distribution of taggants for low layer thickness (25 µm) is non-random, but becomes random for higher layer thickness, and is repeatably random for 100 µm layer samples. This suggests that use of the taggants must take into account the desired layer thickness, which can have a major influence on both the quantity and distribution of taggants. Experiment evidence indicates the embedded taggants don’t affect the thermal properties of the resin parts since the concentration is low. Mechanical strength was reduced by the existence of the taggant, however, only less than 5% under both room temperature and elevated temperatures up to 220 ºC. The amount of taggants embedded links to the mechanical properties, with the minimal amount of taggants resulting in the lowest strength decrease. Declarations Funding This work was supported by the EPSRC (Grant number EP/V051040/1, EP/T02593X/1, and the Strategic Equipment Grant EP/S010076/1). Conflicts of interest/Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Author Contribution Bochuan Liu: writing – original draft, methodology, investigation, data analysisPaul Wilson: writing – methods for X-Ray CT, scanning and analysis of X-Ray CT dataMark Williams: methods for X-ray CT, writing, proof readingGregory Gibbons: writing – spatial point analysis statistical significance, proof reading Acknowledgement The X-Ray Computed Tomography (XCT) data used in this article was acquired using the Free-at-Point-of-Access scheme at the National Facility for X-Ray Computed Tomography (NXCT) and carried out at the Centre for Imaging, Metrology, and Additive Technologies (CiMAT) at the University of Warwick under the Engineering and Physical Sciences Research Council (EPSRC) Project Number (EP/T02593X/1). This work was also supported by the EPSRC [EP/V051040/1] and the Strategic Equipment Grant EP/S010076/1. 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JOM 71:4362–4369. https://doi.org/10.1007/s11837-019-03444-5 Al-Nabulsi Z, Mottram JT, Gillie M, et al (2021) Mechanical and X ray computed tomography characterisation of a WAAM 3D printed steel plate for structural engineering applications. Constr Build Mater 274:121700. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.121700 Yang X, Gibbons GJ, Tanner DA, et al (2023) Scan strategy induced microstructure and consolidation variation in the laser-powder bed fusion (L-PBF) additive manufacturing of low alloy 20MnCr5 steel. Mater Des 232:112160. https://doi.org/https://doi.org/10.1016/j.matdes.2023.112160 Feldkamp LA, Davis LC, Kress JW (1984) Practical cone-beam algorithm. J Opt Soc Am A 1:612–619. https://doi.org/10.1364/JOSAA.1.000612 Aznarte Garcia E, Qureshi AJ, Ayranci C (2018) A study on material-process interaction and optimization for VAT-photopolymerization processes. Rapid Prototyp J 24:1479–1485. https://doi.org/10.1108/RPJ-10-2017-0195 Naik DL, Kiran R (2018) On anisotropy, strain rate and size effects in vat photopolymerization based specimens. Addit Manuf 23:181–196. https://doi.org/https://doi.org/10.1016/j.addma.2018.08.021 Gibson I, Rosen DW, Stucker B, Khorasani M (2021) Additive manufacturing technologies, Third. Springer, Cham, Switzerland Balani K, VV, AA and NR (2014) Physical, Thermal, and Mechanical Properties of Polymers. In: Biosurfaces. pp 329–344 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6627651\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":458456775,\"identity\":\"5656d0a2-584b-43f7-b31c-075a04ff0b89\",\"order_by\":0,\"name\":\"Bochuan Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Warwick Manufacturing Group (WMG), University of Warwick\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bochuan\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":458456776,\"identity\":\"a635d140-9ff1-4cef-bfce-d897ef8c7343\",\"order_by\":1,\"name\":\"Paul F. Wilson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Warwick Manufacturing Group (WMG), University of Warwick\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Paul\",\"middleName\":\"F.\",\"lastName\":\"Wilson\",\"suffix\":\"\"},{\"id\":458456777,\"identity\":\"a0799d52-ddca-4a58-b8d4-db0f3f3c47fb\",\"order_by\":2,\"name\":\"Mark A. Williams\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Warwick Manufacturing Group (WMG), University of Warwick\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mark\",\"middleName\":\"A.\",\"lastName\":\"Williams\",\"suffix\":\"\"},{\"id\":458456778,\"identity\":\"fbfe1c5f-d723-48b9-9309-53a4bfc38d0b\",\"order_by\":3,\"name\":\"Gregory J. Gibbons\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC/gbGBww/DA4kANlAFhCwASEYSODQInGAIYGxB6KF2YAoLUBVCcwMDGAtbFBFRGkpuJPHL93+rJqn5l5iH/uxBIYfNQyJMxvwaTF4Viw554zZbZ5jxYltPGkHGHuOMSTOxmGLIdAvzDIGhxM33Mhhu53DlpDbJsHewMDbwJA4D5fDQFp4wFrSnxXn/INoYfyLVwsLTEuCGXNuG0gL2wFmkC24HCZxmO3AwR6QX2bkGEv/7UuoB/ol4bDMMQljXN7nb29ufPDjDzDEJNIffpzxLcFYvv2Y4cM3NTayMw7gsAYUKRiCB3DHyigYBaNgFIwCYgAAxAJcQP6izboAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Warwick Manufacturing Group (WMG), University of Warwick\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Gregory\",\"middleName\":\"J.\",\"lastName\":\"Gibbons\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-09 10:23:25\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6627651/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6627651/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83215764,\"identity\":\"60613546-c3d8-4261-901d-81dc29fedeea\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 09:06:17\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":255614,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMicrotaggant®shows a colour layer sequence under optical microscopy\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/19a9b96908073db525aa2502.jpeg\"},{\"id\":83213934,\"identity\":\"a2c47d29-4f9e-4243-a6df-1c53ed279e0d\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":399281,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAfterglow features shown by Microtaggant®: a) under 365 nm UV light, b) under 980 nm laser pen\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/ab940d5deac747b59e17bc63.png\"},{\"id\":83213926,\"identity\":\"7c2ffdc5-4393-4e4d-8ff4-450d68941008\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":23241,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEmbedded taggant detected by Keyence VHX700 optical microscope\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/581c05d7e1e407b90c2b006f.jpg\"},{\"id\":83215219,\"identity\":\"5fd11c69-189a-416b-889a-a8ee78ba459d\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:58:17\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":152293,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eXY Positions of Taggants along Z-axis of each sample: blue 25 μm, red 50μm, green 100 μm layer samples\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/11a94599c2bd3b8e6cc08a96.png\"},{\"id\":83213936,\"identity\":\"18ec041b-917f-4bf0-9dc9-4e0f41b0a725\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":14324,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe count of taggants in each cell: blue 25 μm, red 50μm, green 100 μm layer samples\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/a57945a9f12e846925f9b1b3.png\"},{\"id\":83213932,\"identity\":\"3d24c31e-e8d7-492e-b0c9-484ae92c6e6d\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":119591,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eQuadrat statistics of sample 100µm #3 taggant distribution\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/d9376c3274a09ad2800da145.png\"},{\"id\":83213924,\"identity\":\"57ccaaf1-c9ff-41b9-a7fc-fc8b4fe5afb5\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49877,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTaggants embedding and detection process\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/8feae801f0cccb5f022bb22b.png\"},{\"id\":83215225,\"identity\":\"8ba3a73a-6af3-44e5-aa7c-21e12d4930e4\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:58:17\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":30922,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeat flow curves of resin samples with and without taggant embedded\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/1caa90828703c9cbc1f9f8aa.png\"},{\"id\":83213937,\"identity\":\"d1a5d558-7405-42d3-ad16-28ac5ce419d9\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:50:17\",\"extension\":\"jpeg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":63364,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeat flow curves of resin samples with and without taggant embedded: a) pre-soaked at 60 °C; b) pre-soaked at 220 °C\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image9.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/1f8adcae7d6117f15ce8f041.jpeg\"},{\"id\":83215224,\"identity\":\"0dfd5b43-f701-4d4c-b6f9-e504c4d1d7d7\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:58:17\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":8961,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of UTS for samples printed with and without embedded taggants – room temperature testing\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/0e9d410b43f4704fdc877ad1.png\"},{\"id\":83216709,\"identity\":\"c336141b-659d-4002-8281-060410fa33cf\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 09:14:17\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":16546,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of UTS for samples printed with and without embedded taggants – elevated temperature testing\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/431a1bb9d0014f99c96d76d9.png\"},{\"id\":83216879,\"identity\":\"4dd248a3-f2c7-4ac3-903a-5b2445918fcb\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 09:22:18\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2020334,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6627651/v1/4188ac8e-04d3-4c85-9b55-ff82acf16b49.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Embedding of taggants in the vat photo-polymerisation additive manufacturing process for anti-counterfeiting measures\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAdditive manufacturing (AM), also known as 3D printing, has gained significant interest from industry and academia [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Since the 1980s, a spectrum of AM technologies has been developed [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e], and among these types of AM processes, vat photo-polymerisation (VPP) is one of the most popular techniques that has obtained attention from a vast range of the fields including medicine and dentistry, biomedical engineering, metamaterial, injection moulding, jewellery, and consumer products, etc. [\\u003cspan additionalcitationids=\\\"CR4 CR5\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. The principle of VPP is to cure the photosensitive liquid resin layer by layer to fabricate a solid part [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe rapid adoption of AM technology has an undesirable side effect, where counterfeit parts have become easier to obtain provided they have the suitable computer-aided design (CAD) models and 3D printers [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. The CAD models can be downloaded from the internet or obtained from reverse engineering, and nowadays many 3D printers are low-cost and easily accessible. This AM supply chain integrity weakness could result in premature failure of a counterfeit component manufactured by AM, resulting in image, brand and financial loss and damage to a manufacturing company [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAs a consequence, methods to embed various security features in AM parts have been developed. Some of these are embedded in the 3D CAD files in the design step [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], and some are embedded in the printed components, such as fluorescent quantum dots [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], directly printed quick response (QR) code [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], multiple-material QR code [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], surface fingerprint [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], crystallographic orientation [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] and polymer crystallinity [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], etc..\\u003c/p\\u003e \\u003cp\\u003eOne of the popular protections in the forensics field is the taggant, which allows an object to be identified, tracked, and traced [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Taggants are usually introduced into components at a dopant level of 5% or less, making them generally affordable [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. However, the main disadvantage of taggants is they are easy to detect and replicate if too simple and obvious [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. The wide availability and low cost of taggant materials makes it easier to be duplicated.\\u003c/p\\u003e \\u003cp\\u003eThis study aims to develop a \\u0026ldquo;double lock\\u0026rdquo; system to embed taggants into the VPP printed components. The first lock exists physically, and the second one exists digitally, leading to a security feature nearly impossible to reproduce, but easy to detect. This work could help to decrease the difficulty to identify counterfeit goods and prevent damage to the manufacturing companies. The distribution of taggants within the parts, and their effect on the printed part\\u0026rsquo;s mechanical and thermal properties are investigated.\\u003c/p\\u003e\"},{\"header\":\"2. Materials\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Taggant\\u003c/h2\\u003e \\u003cp\\u003eSince the primary aim of this research is to create a practicable methodology to embed a \\u0026ldquo;hash\\u0026rdquo; in the VPP process, benefiting the AM supply chain, a commercially available forensic taggant is preferred for this purpose. Existing forensic taggants include physical, spectroscopic, chemical and DNA taggants with different analysis methods, uses, advantages and disadvantages [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Microtaggant\\u0026reg; (Microtrace LLC, USA), a physical taggant which contains multiple levels of security within each particle was chosen for this study.\\u003c/p\\u003e \\u003cp\\u003eAs a physical taggant, it is made unique by a specific size, appearance, or structural arrangement [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Such encoding mechanisms are also described as \\u0026ldquo;graphical\\u0026rdquo; as the detection and analysis is usually achieved by visual methods [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Microtaggant\\u0026reg; comprises a colour layer sequence, shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, where the colour layer can be converted to a unique numeric code (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The code can then be registered against a particular owner on an electronic database [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMicrotaggant\\u0026reg;\\u0026rsquo;s colour coding values suggested by Microtrace, but can be user-defined\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBlack\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGreen\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBrown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBlue\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eViolet\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOrange\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGrey\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYellow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eMicrotaggant\\u0026reg; is also paired with infrared radiation (IR), ultraviolet (UV) and afterglow features, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. This enables investigators to perform on-the-spot, non-destructive field testing with handheld detectors. Microtaggant\\u0026reg; is stable at up to 250\\u0026deg;C, and up to 350\\u0026deg;C for short exposure, which can be used in explosives detection [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. No licensing agreement is required to use Microtaggant\\u0026reg;.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eMicrotrace provides Microtaggant\\u0026reg; in a range of particle sizes from 20 to 1,200 \\u0026micro;m. Considering the VPP\\u0026rsquo;s layer thickness to be used in this study (25\\u0026ndash;100 \\u0026micro;m), particle sizes between 38 to 75 microns were acquired. This size range gave roughly 2\\u0026nbsp;million particles per gram.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Resin\\u003c/h2\\u003e \\u003cp\\u003eA triacrylate-based amorphous resin - HighTemp DL400 (PhotoCentric Ltd, UK) was used in this study. It appears as a transparent amber liquid and has heat deflection temperature (HDT) of 230\\u0026deg;C when fully cured. This transparency allows the Microtaggant\\u0026reg; to be detected by optical microscopy when embedded inside the resin parts. The high HDT allows the cured parts with taggants to be tested at an elevated temperature close to the stable limit of the Microtaggant\\u0026reg;.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Experiment Methods\",\"content\":\"\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Overview Experimental Plan\\u003c/h2\\u003e \\u003cp\\u003eThe experimental plan consists of 3 main steps: 1) print samples with taggants embedded on a VPP 3D printer with different process parameters; 2) detect the taggants with various technologies and quantify the taggants inside the samples. This step will provide the information for a \\u0026ldquo;double lock\\u0026rdquo; security design \\u0026ndash; physically embedded hash (the taggants) and encrypted and hashed digital files (numeric codes); 3) evaluate the mechanical and thermal properties of the printed samples with taggants embedded.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 3D printing\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.2.1 Resin Preparation\\u003c/h2\\u003e \\u003cp\\u003eThe HighTemp DL400 was warmed in a Gravity Convection Oven (Fisher Scientific Ltd, UK) at 60\\u0026deg;C for 5 hours prior to printing to dissolve any crystallised resin that can occur at or below room temperature. After the resin was fully liquified, Microtaggant\\u0026reg; was added to the resin and stirred at 60\\u0026deg;C and 500 rpm for 20 minutes on a AM4 Heating Magnetic Stirrer (VELP Scientifica Srl, Italy). The taggants loading rate was 0.01 g per 100 ml resin, leading to roughly 20,000 particles in each 100 ml resin.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.2.2 Printing Samples\\u003c/h2\\u003e \\u003cp\\u003eSamples were printed using a Liquid Crystal Nano (Photocentric Ltd, UK), a bottom-up mask projection stereolithography (MPSL) 3D printer using a liquid crystal display mask (LCD) with 4K resolution. Three layer thicknesses were used to compare the quantity of embedded taggants and their effect on the parts\\u0026rsquo; quality. Different process parameters were assigned to each layer thickness to fully cure the resin, but also to avoid over cure and prolonged printing time (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Energy density per unit power was calculated by Eq.\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e \\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\raisebox{1ex}{$unit\\\\:power\\\\:1W*exposure\\\\:time$}\\\\!\\\\left/\\\\:\\\\!\\\\raisebox{-1ex}{$layer\\\\:thickness$}\\\\right.\\\\)\\u003c/span\\u003e \\u003c/span\\u003e Eq.\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePrinting process parameters for each layer thickness\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLayer thickness (\\u0026micro;m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eExposure time for each layer (ms)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEnergy density per unit power (J/mm)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAfter the printing process, all the samples were washed for 10 minutes using Tripropylene glycol monomethyl ether (TPM), then rinsed with tap water for 2 minutes. The samples were then removed from the build substrate, wiped and air-dried before loaded into a 400 nm ultraviolet-visible (UV-Vis) oven (Cure L2 - Photocentric Ltd, UK) with 60\\u0026deg;C heating for 2 hours.\\u003c/p\\u003e \\u003cp\\u003eTensile test specimens designed following ASTM D638 Type V [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e] were built using the parameters in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e (16 for each layer thickness). These specimens were built in the upright position, with the overall length in the Z build direction; and were built directly on the building substrate with no support structures. 8 tensile specimens were built for each layer thickness parameter set with embedded taggants, and 8 without.\\u003c/p\\u003e \\u003cp\\u003eAdditional tensile specimens were built for static tensile testing at elevated temperatures (12 for each test temperature, 6 with embedded taggants and 6 without). These were also built in the upright position and directly on the building substrate at 100 microns layer thickness only.\\u003c/p\\u003e \\u003cp\\u003eIn the same printing job of tensile specimens at 100 microns layer thickness, 36 \\u0026empty;5 x 1.5 mm discs were added for the thermal analysis, 18 of the discs were embedded with taggants and 18 without.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Taggants Detection\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.1 Visual Detection\\u003c/h2\\u003e \\u003cp\\u003eA 365 nm portable UV light and a 980 nm laser pen (Microtrace LLC, USA) were used to detect if the taggants had been successfully embedded into the parts. A VHX7000 digital optical microscope (Keyence (UK) Ltd, UK) was used to obtain the numeric value from the colour layer sequence for each individual taggant.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.2 X-Ray Computed Tomography\\u003c/h2\\u003e \\u003cp\\u003eX-ray computed tomography (XCT) was used to extract and characterise the size, number and position of taggants within the samples, a technique perfectly suited to evaluating and quantifying materials across engineering sectors [\\u003cspan additionalcitationids=\\\"CR25 CR26\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. A total of 9 different tensile test samples were scanned using XCT, three at each of the layer thickness conditions that can be observed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, 100\\u0026micro;m, 50\\u0026micro;m, and 25\\u0026micro;m. The central-most section of each was scanned on a Tescan Unitom XL system (Tescan-Orsay, Brno), at the Centre for Imaging, Metrology, and Additive Technologies (CiMAT). All samples were scanned at the same settings using the proprietary scanning software, Acquila (Tescan-Orsay, Brno), using a polychromatic source with a tungsten target. A beam voltage of 80kV was used at a power of 15W. A total of 2279 projections were acquired with a Source-Detector Distance (SDD) of 1250mm, an exposure time of 0.51s, 2 frame averages and at a voxel size of 3\\u0026micro;m. The datasets for all nine samples were reconstructed in Panthera (Tescan-Orsay, Brno) using a standard FDK algorithm [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], producing a set of tiff image stacks for each sample representing a 5mm section of the centre of each sample.\\u003c/p\\u003e \\u003cp\\u003eThese tiff stacks were then analysed in Avizo 2021.2 (Thermo-Fisher Scientific, Waltham) to extract a number of properties for each sample, namely: 1) the volume fraction of taggants; 2) the number of taggants; 3) the volume and position of individual taggants.\\u003c/p\\u003e \\u003cp\\u003eFor all samples, individual taggants were segmented in Avizo using an Interactive Threshold module, with variable settings used to select them as can be observed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Then, segmented noise was removed using an Opening module with 2px cube structuring element with a Neighbourhood of 18px. The surrounding resin was then segmented using the Wand Tool in the segmentation editor of the main resin body, whose settings can be seen in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e also, with a sequence of dilation and erosion of the area to remove noise and fill holes. This produced both label fields for formal analysis.\\u003c/p\\u003e \\u003cp\\u003eVolume fractions were calculated as a percentile of each fraction to the total volume of both fractions. Volume and position characteristics were extracted using the Label Analysis module, extracting Volume3d, BaryCenterX, BaryCenterY, and BaryCenterZ.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThresholding Parameters for Fractions in all Samples in Avizo.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTaggant Threshold\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eResin\\u003c/p\\u003e \\u003cp\\u003eThreshold\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21399\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9773\\u0026ndash;21398\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15158\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5479\\u0026ndash;15159\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18724\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7473\\u0026ndash;18723\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31653\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17533\\u0026ndash;31652\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38340\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18971\\u0026ndash;38339\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22291\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9198\\u0026ndash;22290\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34328\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20408\\u0026ndash;34327\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31207\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18971\\u0026ndash;31206\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23182\\u0026ndash;65535\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11497\\u0026ndash;23181\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Property Evaluation\\u003c/h2\\u003e \\u003cp\\u003eThe room temperature tensile test samples were tested to destruction by static tension testing at 1 mm/min using an Instron 3367 test system (Instron UK Ltd, UK) with 30kN static loading cell and Instron 2630\\u0026thinsp;\\u0026minus;\\u0026thinsp;102 clip-on extensometer. The elevated temperature tensile testing was performed at 1 mm/min using an Instron 5985 test system (Instron UK Ltd, UK) with 10kN static loading cell, environmental chamber and Instron 2663\\u0026thinsp;\\u0026minus;\\u0026thinsp;821 advanced video extensometer, testing at temperatures 23, 60, 100, 140, 180 and 220\\u0026deg;C to destruction. When the environmental chamber reached the desired testing temperature, the samples were allowed an additional 5 minutes soaking time before the test start. 6 disc samples (3 with taggants and 3 without) were stored inside the environmental chamber at the same time while tensile testing was carried out, to obtain the same thermal history as the tensile specimens.\\u003c/p\\u003e \\u003cp\\u003eDifferential Scanning Calorimetry (DSC) was performed on the disc samples using a DSC 1 (Mettler Toledo UK, UK) thermal analysis system. The samples were measured in aluminium pans with lids and heated under a nitrogen atmosphere from 25\\u0026deg;C to 250\\u0026deg;C at 20\\u0026deg;C/min. Upon reaching 250\\u0026deg;C, the samples were heated isothermally for 5 minutes, then cooled down to 25\\u0026deg;C at 20\\u0026deg;C/min. The same method was used on printed resin samples with and without taggants.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Taggants Detection\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1.1 Taggant Existence Checked by Visual Detection\\u003c/h2\\u003e \\u003cp\\u003eIn the tensile test specimens built using 25, 50 and 100 \\u0026micro;m layer thickness, the taggants were detected in all the samples using 980nm light exposure. They were also detected using 365nm UV light exposure. These observations demonstrate that the Microtaggant\\u0026reg; can be embedded in the resin parts using the VPP process, even when the layer thickness is smaller than the taggants\\u0026rsquo; particle size. When printing at 25 \\u0026micro;m layer thickness, the taggant was transported into the gap between the aluminium build plate and the polymer vat sealing film when the gap was much greater than 25 \\u0026micro;m during the resin layer filling stage of the VPP process cycle, and was then pushed into the soft vat film or previously printed layers when the curing gap was closed down to the 25 \\u0026micro;m layer thickness. The embedded taggant, shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, gave the numeric values to form the first part of the digital hash.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1.2 Volume Fraction of Taggants\\u003c/h2\\u003e \\u003cp\\u003eThe volume fraction of taggants for each sample can be found in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Overall, the volume fraction of taggants in each sample is very low, but appears to increase with coarser layer thickness.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eVolume Fraction of Taggants in each Sample\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample Layer Thickness\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVolume Fraction of Taggants (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVolume Fraction of Resin (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0004\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99.9996\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0008\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99.9992\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0048\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99.9952\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1.3 Dimensions, Quantity, and Position of Taggants\\u003c/h2\\u003e \\u003cp\\u003eThe number of taggants and their volumetric data can be found in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. Overall, the number of taggants appears to scale with the layer thickness, with coarser thicknesses resulting in the retainment of more taggants. The size of taggants in each sample appears to be quite variable however, with a wide variety of taggant sizes being present in each sample with a large standard deviation for each. This appears to imply overall that coarser layer thicknesses result in greater retention of taggants, but the size of those taggants is also variable.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eProperties of Taggants in Samples\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo. of Taggants\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAverage Volume of Taggants (mm\\u003csup\\u003e3\\u003c/sup\\u003e) x10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;5\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026micro;m #3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1.4 Spatial Point Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe position of taggants on each sample can also be observed in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. A quadrat statistical analysis was used to examine the evenness of distribution of the taggants. By applying a 3 x 3 grid, the count of observations that fall within a given cell is presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eTaggant location appears to be biased towards the corners of each sample, with relatively few taggants being apparent within the centre of the samples. This also appears to be related to the layer thickness, with more central taggants being apparent within the coarser layer thickness samples.\\u003c/p\\u003e \\u003cp\\u003eTesting the observed quadrant distributions for randomness, modelling each distribution using the Poisson distribution and performing a c\\u003csup\\u003e2\\u003c/sup\\u003e test for randomness between the expected and observed distributions was performed. The results of this test for each distribution are given in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. The c\\u003csup\\u003e2\\u003c/sup\\u003e test is made for the 5% probability level. The test was performed using a 5 x 5 square quadrant. The analysis was performed in R (The R Project), using the \\u0026ldquo;quadrat.test\\u0026rdquo; function within the Spatstat spatial point pattern analysis tool.\\u003c/p\\u003e \\u003cp\\u003eFrom this analysis, none of the 25 \\u0026micro;m sample distributions are random (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05, null hypothesis is rejected) and only one sample for the 50 \\u0026micro;m distributions (Sample 3) is random (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, null hypothesis is accepted). For the 100 \\u0026micro;m samples, all three are statistically random (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResults of c\\u003csup\\u003e2\\u003c/sup\\u003e test for randomness for the 25\\u0026micro;m, 50\\u0026micro;m and 100\\u0026micro;m distributions\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTest\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSample 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSample 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSample 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ec\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.143\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.796\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.723\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.923\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ec\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e42.167\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.578\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.606\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.025\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100 \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ec\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47.556\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47.400\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1.5 Double Lock System\\u003c/h2\\u003e \\u003cp\\u003eFrom the XCT data, the position of the embedded taggants can be obtained and presented in XYZ coordinates. These coordinates values form the second part of the digital hash. For example, a taggant with colour code \\u0026ldquo;09290\\u0026rdquo; was detected in the part at X\\u0026thinsp;=\\u0026thinsp;3.046 mm, Y\\u0026thinsp;=\\u0026thinsp;2.576 mm and Z\\u0026thinsp;=\\u0026thinsp;1.367 mm would be assigned a unique digital code: 09290304625761367.\\u003c/p\\u003e \\u003cp\\u003eAnother digital hash can be obtained from quadrat statistics of the taggant distribution in any given sample. For sample 100\\u0026micro;m #3, the count of taggants is presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. Combining the number from top left cell to bottom right cell could generate a digital code: 233306631.\\u003c/p\\u003e \\u003cp\\u003eAs can be seen from Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, it would only be possible to use the c\\u003csup\\u003e2\\u003c/sup\\u003e value as a digital lock for 100 \\u0026micro;m samples as it is too variable between samples prepared using the same methodology and taggant dosing level for 25 and 50 \\u0026micro;m layer thickness. This is as a result of the low level of taggants found in the spatial distributions in these samples.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e illustrates the taggants embedding and detection process for a printed component. At the manufacturer side, after the printing stage, XCT scan is preformed to quantify and locate the taggants and their spatial distribution, creating the 2nd part of the digital hash. The taggants\\u0026rsquo; colour code is then obtained using optical microscopy to complete the digital hash for each taggant. The component and the digital hash are then sent to the customer separately, while the digital hash can be stored on a blockchain. When the customer receives the product, decoding will be performed first to locate the taggants in the component, then using the visual detection methods to check if the taggants are in the correct place, and if the colour code matches the digital hash.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn some geometry features that optical microscopy cannot obtain an image, or in non-transparent resin parts, it would be difficult to obtain the numeric value from the taggants\\u0026rsquo; colour layer. In this case, the position coordinates and spatial point analysis acquired from XCT will form the digital hash, and to decode and trace the taggant, XCT scanning would be required at the customer end. This approach also applies to the digital hash generated by quadrat statistics as both ends need to perform XCT scanning to match the code. To ensure the method is scan-agnostic, a master taggant will be identified, and other taggants will have coordinates relative to the master taggant. This is to avoid end users using a different scanning coordinate system from the manufacturer side and generating different results.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Thermal analysis\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e illustrated the heat flow comparison for the 100 \\u0026micro;m layer thickness printed samples with and without taggant embedded, and no significant difference was observed. Although the taggant is a different material and could have different material properties, it did not change the thermal properties of the resin samples due to very low concentration and non-reactivity between the taggant and the resin.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSince the taggant may have different thermal conductivity, it could act differently when preheated. Samples which were pre-soaked in the elevated temperatures were tested in DSC and the heat flow results from 60 and 220\\u0026deg;C pre-soak samples are presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e. These samples were selected as they presented two ends of the elevated temperature range. As for the non-preheated samples, embedded taggants did not cause visible differences in the thermal properties of printed resin parts.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Static Tensile Testing\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e shows the Ultimate Tensile Strength (UTS) values of tensile test specimens built under each layer thickness, with and without taggants.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWithout embedded taggants, samples built with layer thickness 50 \\u0026micro;m compared to the 100 \\u0026micro;m showed an increase in UTS from 83.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4 MPa to 85.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 MPa, could be due to the increased energy density [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. However, samples built with layer thickness 25 \\u0026micro;m compared to the 50 \\u0026micro;m showed a slight drop in UTS from 85.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4 MPa to 84.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 MPa, even when the energy density is higher, although this change is within the bounds of the statistical error, so is insignificant. Lower layer thickness would cause the curing light source to penetrate the cured layer, and the previously cured layers would be exposed multiple times. This could induce internal stress in the samples during the printing process [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e], and affect the mechanical strength.\\u003c/p\\u003e \\u003cp\\u003eThe VPP process utilises the liquid photopolymer to undergo chemical reaction and rapid curing under certain light irradiation. With taggants existing in the liquid resin, photoinduced polymerisation and chemical reaction could be interrupted or impaired [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The embedded taggants inside the parts might also prevent further crosslinking during post-curing. This was evidenced by the tensile test results, with higher taggant densities resulting in lower mechanical performance. The 100 \\u0026micro;m layer sample had the most taggants, and displayed the lowest UTS; the 25 \\u0026micro;m layer sample had the least taggants, and displayed the largest UTS. All the samples with taggants inside presented lower UTS comparing with their same layer thickness counterparts, with reductions of 3.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 %, .9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 % ad 1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 % fr the 100, 50 and 25 \\u0026micro;m layer thicknesses. The lower % of taggants in the 25 \\u0026micro;m layer thickness samples may have overcome the potential internal stress caused by multiple exposures, and led to a higher UTS compared to the 50 \\u0026micro;m layer thickness samples.\\u003c/p\\u003e \\u003cp\\u003eThe tensile tests at elevated temperature were carried out on 100 \\u0026micro;m layer thickness samples only, and the results are presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e. Similar to other polymer materials, the resin samples\\u0026rsquo; UTS decreased with increasing testing temperature [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. The existence of the taggant did not affect the UTS within statistically significant levels, except for a 3.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 % (3 \\u003csup\\u003eo\\u003c/sup\\u003eC) and a 17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2 % (80\\u0026deg;C) relative difference in UTS between the samples with and without taggants, although this variation could be due to the small sample size (6 samples per test set).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eThis study has proven that the Microtaggant\\u0026reg; can be successfully embedded in the resin parts via the vat photo-polymerisation process at 25, 50 and 100 \\u0026micro;m layer thickness. The taggants can be successfully detected by optical methods and XCT. Both physical hash and digital hash generated in this study produced a \\u0026ldquo;double lock\\u0026rdquo; system which is impossible to reproduce.\\u003c/p\\u003e \\u003cp\\u003eXCT scanning has shown that the quantity of taggants within each sample was largely dependent on the layer thickness, which also has an influence on the distribution of them within each sample. The distribution of taggants for low layer thickness (25 \\u0026micro;m) is non-random, but becomes random for higher layer thickness, and is repeatably random for 100 \\u0026micro;m layer samples. This suggests that use of the taggants must take into account the desired layer thickness, which can have a major influence on both the quantity and distribution of taggants.\\u003c/p\\u003e \\u003cp\\u003eExperiment evidence indicates the embedded taggants don\\u0026rsquo;t affect the thermal properties of the resin parts since the concentration is low. Mechanical strength was reduced by the existence of the taggant, however, only less than 5% under both room temperature and elevated temperatures up to 220 \\u0026ordm;C. The amount of taggants embedded links to the mechanical properties, with the minimal amount of taggants resulting in the lowest strength decrease.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis work was supported by the EPSRC (Grant number EP/V051040/1, EP/T02593X/1, and the Strategic Equipment Grant EP/S010076/1).\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eConflicts of interest/Competing interests\\u003c/strong\\u003e \\u003cp\\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eBochuan Liu: writing \\u0026ndash; original draft, methodology, investigation, data analysisPaul Wilson: writing \\u0026ndash; methods for X-Ray CT, scanning and analysis of X-Ray CT dataMark Williams: methods for X-ray CT, writing, proof readingGregory Gibbons: writing \\u0026ndash; spatial point analysis statistical significance, proof reading\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe X-Ray Computed Tomography (XCT) data used in this article was acquired using the Free-at-Point-of-Access scheme at the National Facility for X-Ray Computed Tomography (NXCT) and carried out at the Centre for Imaging, Metrology, and Additive Technologies (CiMAT) at the University of Warwick under the Engineering and Physical Sciences Research Council (EPSRC) Project Number (EP/T02593X/1). This work was also supported by the EPSRC [EP/V051040/1] and the Strategic Equipment Grant EP/S010076/1.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData sets generated during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eLipson H, Kurman M (2013) Fabricated: The new world of 3D printing. John Wiley \\u0026amp; Sons\\u003c/li\\u003e\\n\\u003cli\\u003eGao W, Zhang Y, Ramanujan D, et al (2015) The status, challenges, and future of additive manufacturing in engineering. Computer-Aided Design 69:65\\u0026ndash;89. https://doi.org/10.1016/J.CAD.2015.04.001\\u003c/li\\u003e\\n\\u003cli\\u003eZheng X, Lee H, Weisgraber TH, et al (2014) Ultralight, ultrastiff mechanical metamaterials. Science (1979) 344:1373\\u0026ndash;1377. https://doi.org/10.1126/science.1252291\\u003c/li\\u003e\\n\\u003cli\\u003eChen D, Yan P, Lv B, et al (2018) Parallel reaction monitoring to improve the detection performance of carcinogenic 4-methylimidazole in food by liquid chromatography-high resolution mass spectrometry coupled with dispersive micro solid-phase extraction. Food Control 88:1\\u0026ndash;8. https://doi.org/10.1016/J.FOODCONT.2017.12.021\\u003c/li\\u003e\\n\\u003cli\\u003eZhang F, Zhu L, Li Z, et al (2021) The recent development of vat photopolymerization: A review. Addit Manuf 48:102423\\u003c/li\\u003e\\n\\u003cli\\u003eMelchels FPW, Feijen J, Grijpma DW (2010) A review on stereolithography and its applications in biomedical engineering. Biomaterials 31:6121\\u0026ndash;6130. https://doi.org/10.1016/J.BIOMATERIALS.2010.04.050\\u003c/li\\u003e\\n\\u003cli\\u003eJacobs PF (1992) Rapid prototyping \\u0026amp; manufacturing: fundamentals of stereolithography. Society of Manufacturing Engineers\\u003c/li\\u003e\\n\\u003cli\\u003eWei C, Sun Z, Huang Y, Li L (2018) Embedding anti-counterfeiting features in metallic components via multiple material additive manufacturing. Addit Manuf 24:. https://doi.org/10.1016/j.addma.2018.09.003\\u003c/li\\u003e\\n\\u003cli\\u003eZeltmann SE, Gupta N, Tsoutsos NG, et al (2016) Manufacturing and security challenges in 3D printing. Jom 68:1872\\u0026ndash;1881\\u003c/li\\u003e\\n\\u003cli\\u003eChen F, Mac G, Gupta N (2017) Security features embedded in computer aided design (CAD) solid models for additive manufacturing. Mater Des 128:182\\u0026ndash;194. https://doi.org/10.1016/J.MATDES.2017.04.078\\u003c/li\\u003e\\n\\u003cli\\u003eIvanova O, Elliott A, Campbell T, Williams CB (2014) Unclonable security features for additive manufacturing. Addit Manuf 1:. https://doi.org/10.1016/j.addma.2014.07.001\\u003c/li\\u003e\\n\\u003cli\\u003eChen F, Luo Y, Tsoutsos NG, et al (2019) Embedding Tracking Codes in Additive Manufactured Parts for Product Authentication. Adv Eng Mater 21:1800495. https://doi.org/https://doi.org/10.1002/adem.201800495\\u003c/li\\u003e\\n\\u003cli\\u003eSalas D, Ebeperi D, Elverud M, et al (2022) Embedding hidden information in additively manufactured metals via magnetic property grading for traceability. Addit Manuf 60:103261. https://doi.org/https://doi.org/10.1016/j.addma.2022.103261\\u003c/li\\u003e\\n\\u003cli\\u003eGao Y, Wang W, Jin Y, et al (2021) ThermoTag: A Hidden ID of 3D Printers for Fingerprinting and Watermarking. IEEE Transactions on Information Forensics and Security 16:2805\\u0026ndash;2820. https://doi.org/10.1109/TIFS.2021.3065225\\u003c/li\\u003e\\n\\u003cli\\u003eSofinowski K, Wittwer M, Seita M (2022) Encoding data into metal alloys using laser powder bed fusion. Addit Manuf 52:102683. https://doi.org/https://doi.org/10.1016/j.addma.2022.102683\\u003c/li\\u003e\\n\\u003cli\\u003eCox JR, Kipling I, Gibbons GJ (2023) Ensuring supply chain integrity for material extrusion 3D printed polymer parts. Addit Manuf 62:103403. https://doi.org/https://doi.org/10.1016/j.addma.2023.103403\\u003c/li\\u003e\\n\\u003cli\\u003eGooch J, Daniel B, Abbate V, Frascione N (2016) Taggant materials in forensic science: A review. TrAC Trends in Analytical Chemistry 83:49\\u0026ndash;54. https://doi.org/10.1016/J.TRAC.2016.08.003\\u003c/li\\u003e\\n\\u003cli\\u003eNatan MJ (2003) Surface enhanced spectroscopy-active composite nanoparticles\\u003c/li\\u003e\\n\\u003cli\\u003eFlank S (2017) Legal Issues in IP Protection for Additive Manufacturing. Tex A\\u0026amp;M J Prop L 4:1\\u003c/li\\u003e\\n\\u003cli\\u003eDeisingh AK (2005) Pharmaceutical counterfeiting. Analyst 130:271\\u0026ndash;279. https://doi.org/10.1039/B407759H\\u003c/li\\u003e\\n\\u003cli\\u003ePaunescu D, Stark WJ, Grass RN (2016) Particles with an identity: Tracking and tracing in commodity products. Powder Technol 291:344\\u0026ndash;350. https://doi.org/10.1016/J.POWTEC.2015.12.035\\u003c/li\\u003e\\n\\u003cli\\u003eSwiegers GF, Bootle BW, George GM (2012) Method and system for identifying items\\u003c/li\\u003e\\n\\u003cli\\u003eASTM International (2014) ASTM Standard D638-14 Standard Test Method for Tensile Properties of Plastics. ASTM D638-14\\u003c/li\\u003e\\n\\u003cli\\u003eCooper D, Thornby J, Blundell N, et al (2015) Design and manufacture of high performance hollow engine valves by Additive Layer Manufacturing. Mater Des 69:44\\u0026ndash;55. https://doi.org/https://doi.org/10.1016/j.matdes.2014.11.017\\u003c/li\\u003e\\n\\u003cli\\u003eMathew J, Remy G, Williams MA, et al (2019) Effect of Fe Intermetallics on Microstructure and Properties of Al-7Si Alloys. JOM 71:4362\\u0026ndash;4369. https://doi.org/10.1007/s11837-019-03444-5\\u003c/li\\u003e\\n\\u003cli\\u003eAl-Nabulsi Z, Mottram JT, Gillie M, et al (2021) Mechanical and X ray computed tomography characterisation of a WAAM 3D printed steel plate for structural engineering applications. Constr Build Mater 274:121700. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.121700\\u003c/li\\u003e\\n\\u003cli\\u003eYang X, Gibbons GJ, Tanner DA, et al (2023) Scan strategy induced microstructure and consolidation variation in the laser-powder bed fusion (L-PBF) additive manufacturing of low alloy 20MnCr5 steel. Mater Des 232:112160. https://doi.org/https://doi.org/10.1016/j.matdes.2023.112160\\u003c/li\\u003e\\n\\u003cli\\u003eFeldkamp LA, Davis LC, Kress JW (1984) Practical cone-beam algorithm. J Opt Soc Am A 1:612\\u0026ndash;619. https://doi.org/10.1364/JOSAA.1.000612\\u003c/li\\u003e\\n\\u003cli\\u003eAznarte Garcia E, Qureshi AJ, Ayranci C (2018) A study on material-process interaction and optimization for VAT-photopolymerization processes. Rapid Prototyp J 24:1479\\u0026ndash;1485. https://doi.org/10.1108/RPJ-10-2017-0195\\u003c/li\\u003e\\n\\u003cli\\u003eNaik DL, Kiran R (2018) On anisotropy, strain rate and size effects in vat photopolymerization based specimens. Addit Manuf 23:181\\u0026ndash;196. https://doi.org/https://doi.org/10.1016/j.addma.2018.08.021\\u003c/li\\u003e\\n\\u003cli\\u003eGibson I, Rosen DW, Stucker B, Khorasani M (2021) Additive manufacturing technologies, Third. Springer, Cham, Switzerland\\u003c/li\\u003e\\n\\u003cli\\u003eBalani K, VV, AA and NR (2014) Physical, Thermal, and Mechanical Properties of Polymers. In: Biosurfaces. pp 329\\u0026ndash;344\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Additive manufacturing, Vat photo-polymerisation, Anti-counterfeiting, Taggant, X-Ray computed tomography, Double-lock hash\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6627651/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6627651/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe Additive Manufacturing (AM) industry has grown significantly and attracted global attention. The unique nature of AM, that it can manufacture components from CAD data, made it vulnerable to fraudulent and counterfeit activities. With scanning and reverse engineering technologies advancing, existing authentication methods (such as embedded Quick Response code) become easy to detect and reproduce, and therefore unreliable. This article investigates a novel authenticating method for vat photo-polymerisation process with a double-lock system introduced. Both physical hash (the taggants) and digital hash (generated by X-Ray computed tomography scan) were embedded into each component. A number of digital hashing methods were proposed, including using a combination of the taggant spatial coordinates, colour code and quadrat count, which are impossible to reproduce. Using a 100\\u0026micro;m layer thickness, the χ\\u003csup\\u003e2\\u003c/sup\\u003e value of the distribution can also be used as the digital hash. This study also indicated the embedded physical taggants had no effect on the thermal properties of the parts, and had minimal impact on the mechanical strength, with reductions of 3.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5%, 3.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5% and 1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5% in UTS at room temperature for the 100, 50 and 25 \\u0026micro;m layer thicknesses observed. The UTS for all samples decreased with increasing temperature The existence of the taggant did not affect the UTS within statistically significant levels, except for those tested at 23 \\u003csup\\u003eo\\u003c/sup\\u003eC and 180\\u0026deg;C with 3.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5% and 17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2% observed variation in UTS between samples with and without taggants.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Embedding of taggants in the vat photo-polymerisation additive manufacturing process for anti-counterfeiting measures\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-21 08:50:12\",\"doi\":\"10.21203/rs.3.rs-6627651/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d470fd7f-55e9-4853-a42f-c26939b33b8e\",\"owner\":[],\"postedDate\":\"May 21st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-10T04:09:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-21 08:50:12\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6627651\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6627651\",\"identity\":\"rs-6627651\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}